Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	77,656
2	South Dakota	68,632
3	Iowa	54,959
4	Wisconsin	53,174
5	Nebraska	47,845
6	Utah	44,803
7	Idaho	44,137
8	Mississippi	43,904
9	Alabama	42,959
10	Louisiana	42,705
11	Tennessee	42,681
12	Illinois	42,445
13	Arkansas	42,416
14	Montana	40,311
15	Florida	40,209
16	Rhode Island	38,479
17	Missouri	38,315
18	Kansas	38,273
19	Georgia	37,838
20	Nevada	37,452
21	South Carolina	36,997
22	Arizona	36,633
23	Texas	36,579
24	Oklahoma	36,566
25	Minnesota	35,791
26	Wyoming	35,384
27	Indiana	34,689
28	New Jersey	30,261
29	Kentucky	29,699
30	Alaska	29,648
31	New Mexico	28,984
32	North Carolina	28,978
33	New York	28,298
34	Delaware	28,288
35	Maryland	26,516
36	District of Columbia	26,223
37	Massachusetts	26,142
38	Michigan	25,931
39	Colorado	25,919
40	California	25,541
41	Connecticut	24,093
42	Ohio	23,479
43	Virginia	23,200
44	Puerto Rico	23,191
45	Pennsylvania	19,853
46	West Virginia	17,240
47	Washington	17,028
48	Oregon	12,774
49	Hawaii	11,585
50	New Hampshire	9,906
51	Maine	6,245
52	Vermont	4,248

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Dakota	1,659
2	North Dakota	1,625
3	Iowa	1,425
4	Wyoming	1,422
5	Wisconsin	1,325
6	Nebraska	1,206
7	Minnesota	1,005
8	Illinois	995
9	Montana	925
10	Utah	911
11	Rhode Island	866
12	Indiana	817
13	Colorado	782
14	Idaho	776
15	Missouri	728
16	New Mexico	713
17	Kansas	709
18	Michigan	695
19	Alaska	685
20	Arkansas	573
21	Ohio	555
22	Kentucky	548
23	Oklahoma	525
24	Nevada	436
25	Tennessee	426
26	Connecticut	414
27	Texas	393
28	Alabama	392
29	West Virginia	389
30	Mississippi	387
31	New Jersey	387
32	Pennsylvania	376
33	Louisiana	369
34	Massachusetts	360
35	Georgia	298
36	North Carolina	280
37	Arizona	274
38	South Carolina	265
39	Maryland	247
40	Florida	245
41	New York	232
42	Washington	223
43	Delaware	218
44	Oregon	215
45	California	199
46	District of Columbia	198
47	Puerto Rico	195
48	New Hampshire	189
49	Virginia	177
50	Vermont	126
51	Maine	125
52	Hawaii	65

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,857
2	New York	1,718
3	Massachusetts	1,485
4	Connecticut	1,325
5	Louisiana	1,311
6	Mississippi	1,180
7	Rhode Island	1,179
8	District of Columbia	930
9	North Dakota	922
10	Arizona	857
11	Illinois	856
12	Michigan	819
13	Georgia	815
14	Florida	808
15	South Carolina	793
16	Delaware	751
17	Pennsylvania	724
18	Indiana	714
19	Arkansas	710
20	Maryland	704
21	Texas	679
22	Alabama	655
23	South Dakota	640
24	Iowa	617
25	Nevada	610
26	Missouri	560
27	New Mexico	560
28	Tennessee	549
29	Minnesota	505
30	Ohio	484
31	California	459
32	Wisconsin	451
33	North Carolina	450
34	Montana	447
35	Virginia	440
36	Colorado	436
37	Idaho	421
38	Kansas	417
39	Nebraska	397
40	Kentucky	380
41	Oklahoma	374
42	New Hampshire	364
43	Washington	343
44	West Virginia	309
45	Puerto Rico	284
46	Wyoming	219
47	Utah	214
48	Oregon	177
49	Hawaii	156
50	Alaska	123
51	Maine	118
52	Vermont	94

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	23
2	South Dakota	11
3	Wisconsin	11
4	Missouri	10
5	Idaho	9
6	Nebraska	9
7	Alabama	8
8	Tennessee	8
9	Illinois	7
10	Indiana	7
11	Iowa	7
12	Minnesota	7
13	Mississippi	7
14	New Mexico	7
15	Wyoming	7
16	Georgia	6
17	Alaska	5
18	Michigan	5
19	Rhode Island	5
20	Colorado	4
21	Delaware	4
22	Montana	4
23	Pennsylvania	4
24	Texas	4
25	West Virginia	4
26	Arizona	3
27	Arkansas	3
28	Kansas	3
29	Kentucky	3
30	Louisiana	3
31	Ohio	3
32	Oklahoma	3
33	Connecticut	2
34	Florida	2
35	Maryland	2
36	Massachusetts	2
37	Nevada	2
38	New Jersey	2
39	North Carolina	2
40	Puerto Rico	2
41	South Carolina	2
42	Utah	2
43	Maine	1
44	New Hampshire	1
45	New York	1
46	Virginia	1
47	Washington	1
48	California	0
49	District of Columbia	0
50	Hawaii	0
51	Oregon	0
52	Vermont	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	181,434	1	99
Norton	Kansas	179,631	2	99
Chattahoochee	Georgia	176,125	3	99
Bon Homme	South Dakota	172,004	4	99
Buffalo	South Dakota	165,138	5	99
Davidson	Tennessee	55,899	373	88
Richland	South Carolina	44,694	760	75
York	South Carolina	27,885	1808	42
Orange	California	20,642	2319	26
Pierce	Washington	15,176	2618	16

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,208	1	99
Jerauld	South Dakota	6,458	2	99
Hancock	Georgia	5,321	3	99
Emporia city	Virginia	5,238	4	99
Kenedy	Texas	4,950	5	99
Richland	South Carolina	678	1035	67
Davidson	Tennessee	539	1340	57
Orange	California	479	1491	52
York	South Carolina	413	1676	46
Pierce	Washington	307	1987	36

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.